Classification of vascular malformations based on T2 STIR magnetic resonance imaging
DW Nunes, M Hammer, S Hammer, W Uller… - Bildverarbeitung für die …, 2022 - Springer
Bildverarbeitung für die Medizin 2022: Proceedings, German Workshop on Medical …, 2022•Springer
Vascular malformations (VMs) are a rare condition. They can be categorized into high-flow
and low-flow VMs, which is a challenging task for radiologists. In this work, a very
heterogeneous set of MRI images with only rough annotations are used for classification
with a convolutional neural network. The main focus is to describe the challenging data set
and strategies to deal with such data in terms of preprocessing, annotation usage and
choice of the network architecture. We achieved a classification result of 89.47% F1-score …
and low-flow VMs, which is a challenging task for radiologists. In this work, a very
heterogeneous set of MRI images with only rough annotations are used for classification
with a convolutional neural network. The main focus is to describe the challenging data set
and strategies to deal with such data in terms of preprocessing, annotation usage and
choice of the network architecture. We achieved a classification result of 89.47% F1-score …
Zusammenfassung
Vascular malformations (VMs) are a rare condition. They can be categorized into high-flow and low-flow VMs, which is a challenging task for radiologists. In this work, a very heterogeneous set of MRI images with only rough annotations are used for classification with a convolutional neural network. The main focus is to describe the challenging data set and strategies to deal with such data in terms of preprocessing, annotation usage and choice of the network architecture. We achieved a classification result of 89.47% F1-score with a 3D ResNet 18.
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